Tutorial on PCA and approximate PCA and approximate kernel PCA Article Swipe

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Kernel principal component analysis Principal component analysis Computer science Artificial intelligence Pattern recognition (psychology) Kernel (algebra) Kernel method Sparse PCA Machine learning Mathematics Support vector machine Combinatorics

Sanparith Marukatat ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1007/s10462-022-10297-z · OA: W4307911024

Principal Component Analysis (PCA) is one of the most widely used data analysis methods in machine learning and AI. This manuscript focuses on the mathematical foundation of classical PCA and its application to a small-sample-size scenario and a large dataset in a high-dimensional space scenario. In particular, we discuss a simple method that can be used to approximate PCA in the latter case. This method can also help approximate kernel PCA or kernel PCA (KPCA) for a large-scale dataset. We hope this manuscript will give readers a solid foundation on PCA, approximate PCA, and approximate KPCA.